{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07a90dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0f1df1",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ee04456",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('D:/数据分析/第九阶段/模块一/作业/车贷违约预测.csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6dc6165d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <th>品牌</th>\n",
       "      <th>骑车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>货款日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>...</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比列</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601758</td>\n",
       "      <td>65532</td>\n",
       "      <td>78990</td>\n",
       "      <td>84.38</td>\n",
       "      <td>136</td>\n",
       "      <td>20490</td>\n",
       "      <td>45</td>\n",
       "      <td>1981</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>519488</td>\n",
       "      <td>56759</td>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>61</td>\n",
       "      <td>22778</td>\n",
       "      <td>86</td>\n",
       "      <td>1967</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2054139</td>\n",
       "      <td>2036500</td>\n",
       "      <td>2036500</td>\n",
       "      <td>34455</td>\n",
       "      <td>0.99</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>447579</td>\n",
       "      <td>58413</td>\n",
       "      <td>67960</td>\n",
       "      <td>89.02</td>\n",
       "      <td>5</td>\n",
       "      <td>15663</td>\n",
       "      <td>86</td>\n",
       "      <td>1977</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>648134</td>\n",
       "      <td>72317</td>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>76</td>\n",
       "      <td>17242</td>\n",
       "      <td>48</td>\n",
       "      <td>1995</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13813</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458210</td>\n",
       "      <td>50078</td>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>146</td>\n",
       "      <td>14181</td>\n",
       "      <td>45</td>\n",
       "      <td>1974</td>\n",
       "      <td>2018</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>467161</td>\n",
       "      <td>550000</td>\n",
       "      <td>550000</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.18</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>616513</td>\n",
       "      <td>63882</td>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>152</td>\n",
       "      <td>14470</td>\n",
       "      <td>51</td>\n",
       "      <td>1993</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>16225</td>\n",
       "      <td>17700</td>\n",
       "      <td>17700</td>\n",
       "      <td>1475</td>\n",
       "      <td>1.09</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>453368</td>\n",
       "      <td>54013</td>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>34</td>\n",
       "      <td>16556</td>\n",
       "      <td>86</td>\n",
       "      <td>1971</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>12991</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>3207</td>\n",
       "      <td>7.70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>565879</td>\n",
       "      <td>63882</td>\n",
       "      <td>78934</td>\n",
       "      <td>83.61</td>\n",
       "      <td>76</td>\n",
       "      <td>18231</td>\n",
       "      <td>86</td>\n",
       "      <td>1983</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>567382</td>\n",
       "      <td>63882</td>\n",
       "      <td>77654</td>\n",
       "      <td>84.35</td>\n",
       "      <td>13</td>\n",
       "      <td>20706</td>\n",
       "      <td>86</td>\n",
       "      <td>1973</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>53701</td>\n",
       "      <td>197000</td>\n",
       "      <td>197000</td>\n",
       "      <td>4363</td>\n",
       "      <td>3.67</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>531090</td>\n",
       "      <td>110987</td>\n",
       "      <td>165925</td>\n",
       "      <td>69.91</td>\n",
       "      <td>48</td>\n",
       "      <td>23462</td>\n",
       "      <td>67</td>\n",
       "      <td>1972</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     客户编号    已发货款    资产成本  贷款与资产比列   品牌  骑车销售商  车厂  出生日期  货款日期  地区  ...  \\\n",
       "0  601758   65532   78990    84.38  136  20490  45  1981  2018   8  ...   \n",
       "1  519488   56759   65325    89.55   61  22778  86  1967  2018   6  ...   \n",
       "2  447579   58413   67960    89.02    5  15663  86  1977  2018   9  ...   \n",
       "3  648134   72317   99750    73.68   76  17242  48  1995  2018   8  ...   \n",
       "4  458210   50078   65450    79.45  146  14181  45  1974  2018  17  ...   \n",
       "5  616513   63882   79605    82.91  152  14470  51  1993  2018   3  ...   \n",
       "6  453368   54013   62371    89.79   34  16556  86  1971  2018   6  ...   \n",
       "7  565879   63882   78934    83.61   76  18231  86  1983  2018   8  ...   \n",
       "8  567382   63882   77654    84.35   13  20706  86  1973  2018   8  ...   \n",
       "9  531090  110987  165925    69.91   48  23462  67  1972  2018   5  ...   \n",
       "\n",
       "   尚未还清有效贷款总额  已批准贷款总额  已发放贷款总额  每月还款总额  贷款与已还贷款比列  主账户还款期数  次账户还款期数  \\\n",
       "0           0        0        0       0       1.00        0        0   \n",
       "1     2054139  2036500  2036500   34455       0.99       59        0   \n",
       "2           0        0        0       0       1.00        0        0   \n",
       "3           0    13813    13813       0   13814.00    13813        0   \n",
       "4      467161   550000   550000   12863       1.18       42        0   \n",
       "5       16225    17700    17700    1475       1.09       11        0   \n",
       "6       12991   100000   100000    3207       7.70       31        0   \n",
       "7           0        0        0       0       1.00        0        0   \n",
       "8       53701   197000   197000    4363       3.67       45        0   \n",
       "9           0        0        0       0       1.00        0        0   \n",
       "\n",
       "   贷款与已批准贷款比列  总贷款次数与总有效贷款次数比  工作类型  \n",
       "0         1.0            1.00     0  \n",
       "1         1.0            1.33     1  \n",
       "2         1.0            1.00     1  \n",
       "3         1.0            2.00     0  \n",
       "4         1.0            1.06     1  \n",
       "5         1.0            1.50     0  \n",
       "6         1.0            1.33     1  \n",
       "7         1.0            1.00     1  \n",
       "8         1.0            1.33     1  \n",
       "9         1.0            1.00     0  \n",
       "\n",
       "[10 rows x 49 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10371b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    164289\n",
       "1     35428\n",
       "Name: 是否违约, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.是否违约.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f364d72b",
   "metadata": {},
   "source": [
    "## 异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d946c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '客户流失比例')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看数据类型\n",
    "churn = data['是否违约'].value_counts()\n",
    "# label = data['Churn'].value_counts().index\n",
    "label = ['未违约','违约']\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(churn,labels=label,explode=(0.08,0),autopct='%0.1f%%',shadow=True,textprops={'fontsize':20,'color':'black'})\n",
    "plt.title('客户流失比例',size=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59547647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>客户编号</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>535690.886665</td>\n",
       "      <td>6.819341e+04</td>\n",
       "      <td>4.174280e+05</td>\n",
       "      <td>476762.000000</td>\n",
       "      <td>535571.000000</td>\n",
       "      <td>594571.000000</td>\n",
       "      <td>6.710840e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已发货款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>54256.272280</td>\n",
       "      <td>1.297766e+04</td>\n",
       "      <td>1.332000e+04</td>\n",
       "      <td>46977.000000</td>\n",
       "      <td>53703.000000</td>\n",
       "      <td>60247.000000</td>\n",
       "      <td>9.905720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产成本</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>75823.911139</td>\n",
       "      <td>1.892894e+04</td>\n",
       "      <td>3.700000e+04</td>\n",
       "      <td>65714.000000</td>\n",
       "      <td>70922.000000</td>\n",
       "      <td>79159.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>74.643960</td>\n",
       "      <td>1.149048e+01</td>\n",
       "      <td>1.003000e+01</td>\n",
       "      <td>68.730000</td>\n",
       "      <td>76.670000</td>\n",
       "      <td>83.590000</td>\n",
       "      <td>9.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品牌</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>72.698508</td>\n",
       "      <td>6.970618e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>2.610000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>骑车销售商</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>19634.049665</td>\n",
       "      <td>3.493655e+03</td>\n",
       "      <td>1.052400e+04</td>\n",
       "      <td>16505.000000</td>\n",
       "      <td>20333.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>2.480300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车厂</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>69.085766</td>\n",
       "      <td>2.212829e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.560000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>出生日期</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1983.876921</td>\n",
       "      <td>9.805565e+00</td>\n",
       "      <td>1.949000e+03</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>货款日期</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.018000e+03</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2.018000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7.245222</td>\n",
       "      <td>4.481338e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>对接员工编号</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1547.857919</td>\n",
       "      <td>9.749015e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "      <td>2357.000000</td>\n",
       "      <td>3.795000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否填写手机号</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>受否填写身份证</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.023348</td>\n",
       "      <td>1.510067e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否填写护照</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>4.624338e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信用评分</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>291.762544</td>\n",
       "      <td>3.393176e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>8.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.464037</td>\n",
       "      <td>5.283968e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.048414</td>\n",
       "      <td>1.951018e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.440000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>168728.603864</td>\n",
       "      <td>9.638043e+05</td>\n",
       "      <td>-6.678296e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35899.000000</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已批准的贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>222432.313449</td>\n",
       "      <td>2.522528e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已发放贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>222042.040282</td>\n",
       "      <td>2.525814e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62000.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>6.306478e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.027689</td>\n",
       "      <td>3.144277e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>5583.871318</td>\n",
       "      <td>1.686728e+05</td>\n",
       "      <td>-5.746470e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.603285e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中已批准贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7490.970053</td>\n",
       "      <td>1.818362e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.688820e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中已发放贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7374.478036</td>\n",
       "      <td>1.812332e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.688820e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户每月还款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13144.154408</td>\n",
       "      <td>1.524289e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2.564281e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户没用还款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>301.373393</td>\n",
       "      <td>1.304531e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.246710e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.385070</td>\n",
       "      <td>9.573387e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.095956</td>\n",
       "      <td>3.809351e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均贷款期限</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>8.058107</td>\n",
       "      <td>1.386076e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13.190875</td>\n",
       "      <td>2.115686e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款查询次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.203338</td>\n",
       "      <td>6.940867e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否违约</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.177391</td>\n",
       "      <td>3.820002e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.723575</td>\n",
       "      <td>1.136129e-01</td>\n",
       "      <td>9.463821e-02</td>\n",
       "      <td>0.664431</td>\n",
       "      <td>0.741715</td>\n",
       "      <td>0.809512</td>\n",
       "      <td>9.379874e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款总次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.523561</td>\n",
       "      <td>5.356066e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户无效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.415623</td>\n",
       "      <td>4.038380e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户无效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.031835</td>\n",
       "      <td>4.127953e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.447458</td>\n",
       "      <td>4.075544e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>174312.475182</td>\n",
       "      <td>9.813640e+05</td>\n",
       "      <td>-6.678296e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38189.000000</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>229923.283501</td>\n",
       "      <td>2.530977e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67206.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>229416.518318</td>\n",
       "      <td>2.534185e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65085.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>每月还款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13445.527802</td>\n",
       "      <td>1.531618e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2.564281e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1373.357578</td>\n",
       "      <td>2.981642e+04</td>\n",
       "      <td>-1.100003e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>5.000001e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>50595.821783</td>\n",
       "      <td>2.275670e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2927.999594</td>\n",
       "      <td>1.065410e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.980000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已批准贷款比列</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>553.570913</td>\n",
       "      <td>1.141343e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.438913</td>\n",
       "      <td>7.922133e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工作类型</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.487475</td>\n",
       "      <td>5.619145e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count           mean           std           min  \\\n",
       "客户编号            199717.0  535690.886665  6.819341e+04  4.174280e+05   \n",
       "已发货款            199717.0   54256.272280  1.297766e+04  1.332000e+04   \n",
       "资产成本            199717.0   75823.911139  1.892894e+04  3.700000e+04   \n",
       "贷款与资产比列         199717.0      74.643960  1.149048e+01  1.003000e+01   \n",
       "品牌              199717.0      72.698508  6.970618e+01  1.000000e+00   \n",
       "骑车销售商           199717.0   19634.049665  3.493655e+03  1.052400e+04   \n",
       "车厂              199717.0      69.085766  2.212829e+01  4.500000e+01   \n",
       "出生日期            199717.0    1983.876921  9.805565e+00  1.949000e+03   \n",
       "货款日期            199717.0    2018.000000  0.000000e+00  2.018000e+03   \n",
       "地区              199717.0       7.245222  4.481338e+00  1.000000e+00   \n",
       "对接员工编号          199717.0    1547.857919  9.749015e+02  1.000000e+00   \n",
       "是否填写手机号         199717.0       1.000000  0.000000e+00  1.000000e+00   \n",
       "受否填写身份证         199717.0       1.000000  0.000000e+00  1.000000e+00   \n",
       "是否出具驾驶证         199717.0       0.023348  1.510067e-01  0.000000e+00   \n",
       "是否填写护照          199717.0       0.002143  4.624338e-02  0.000000e+00   \n",
       "信用评分            199717.0     291.762544  3.393176e+02  0.000000e+00   \n",
       "主账户贷款次数         199717.0       2.464037  5.283968e+00  0.000000e+00   \n",
       "主账户有效贷款次数       199717.0       1.048414  1.951018e+00  0.000000e+00   \n",
       "主账户中尚未还清有效贷款    199717.0  168728.603864  9.638043e+05 -6.678296e+06   \n",
       "主账户中已批准的贷款      199717.0  222432.313449  2.522528e+06  0.000000e+00   \n",
       "主账户中已发放贷款       199717.0  222042.040282  2.525814e+06  0.000000e+00   \n",
       "次账户贷款次数         199717.0       0.059524  6.306478e-01  0.000000e+00   \n",
       "次账户有效贷款次数       199717.0       0.027689  3.144277e-01  0.000000e+00   \n",
       "次账户中尚未还清有效贷款    199717.0    5583.871318  1.686728e+05 -5.746470e+05   \n",
       "次账户中已批准贷款       199717.0    7490.970053  1.818362e+05  0.000000e+00   \n",
       "次账户中已发放贷款       199717.0    7374.478036  1.812332e+05  0.000000e+00   \n",
       "主账户每月还款         199717.0   13144.154408  1.524289e+05  0.000000e+00   \n",
       "次账户没用还款         199717.0     301.373393  1.304531e+04  0.000000e+00   \n",
       "近六个月新贷款次数       199717.0       0.385070  9.573387e-01  0.000000e+00   \n",
       "近六个月违约次数        199717.0       0.095956  3.809351e-01  0.000000e+00   \n",
       "平均贷款期限          199717.0       8.058107  1.386076e+01  0.000000e+00   \n",
       "第一次贷款距今时间       199717.0      13.190875  2.115686e+01  0.000000e+00   \n",
       "贷款查询次数          199717.0       0.203338  6.940867e-01  0.000000e+00   \n",
       "是否违约            199717.0       0.177391  3.820002e-01  0.000000e+00   \n",
       "贷款与资产比          199717.0       0.723575  1.136129e-01  9.463821e-02   \n",
       "贷款总次数           199717.0       2.523561  5.356066e+00  0.000000e+00   \n",
       "主账户无效贷款次数       199717.0       1.415623  4.038380e+00  0.000000e+00   \n",
       "次账户无效贷款次数       199717.0       0.031835  4.127953e-01  0.000000e+00   \n",
       "无效贷款总次数         199717.0       1.447458  4.075544e+00  0.000000e+00   \n",
       "尚未还清有效贷款总额      199717.0  174312.475182  9.813640e+05 -6.678296e+06   \n",
       "已批准贷款总额         199717.0  229923.283501  2.530977e+06  0.000000e+00   \n",
       "已发放贷款总额         199717.0  229416.518318  2.534185e+06  0.000000e+00   \n",
       "每月还款总额          199717.0   13445.527802  1.531618e+05  0.000000e+00   \n",
       "贷款与已还贷款比列       199697.0    1373.357578  2.981642e+04 -1.100003e+05   \n",
       "主账户还款期数         199717.0   50595.821783  2.275670e+06  0.000000e+00   \n",
       "次账户还款期数         199717.0    2927.999594  1.065410e+05  0.000000e+00   \n",
       "贷款与已批准贷款比列      199717.0     553.570913  1.141343e+05  0.000000e+00   \n",
       "总贷款次数与总有效贷款次数比  199717.0       1.438913  7.922133e-01  1.000000e+00   \n",
       "工作类型            199717.0       0.487475  5.619145e-01  0.000000e+00   \n",
       "\n",
       "                          25%            50%            75%           max  \n",
       "客户编号            476762.000000  535571.000000  594571.000000  6.710840e+05  \n",
       "已发货款             46977.000000   53703.000000   60247.000000  9.905720e+05  \n",
       "资产成本             65714.000000   70922.000000   79159.000000  1.628992e+06  \n",
       "贷款与资产比列             68.730000      76.670000      83.590000  9.500000e+01  \n",
       "品牌                  14.000000      61.000000     130.000000  2.610000e+02  \n",
       "骑车销售商            16505.000000   20333.000000   23000.000000  2.480300e+04  \n",
       "车厂                  48.000000      86.000000      86.000000  1.560000e+02  \n",
       "出生日期              1977.000000    1986.000000    1992.000000  2.000000e+03  \n",
       "货款日期              2018.000000    2018.000000    2018.000000  2.018000e+03  \n",
       "地区                   4.000000       6.000000      10.000000  2.200000e+01  \n",
       "对接员工编号             712.000000    1449.000000    2357.000000  3.795000e+03  \n",
       "是否填写手机号              1.000000       1.000000       1.000000  1.000000e+00  \n",
       "受否填写身份证              1.000000       1.000000       1.000000  1.000000e+00  \n",
       "是否出具驾驶证              0.000000       0.000000       0.000000  1.000000e+00  \n",
       "是否填写护照               0.000000       0.000000       0.000000  1.000000e+00  \n",
       "信用评分                 0.000000      14.000000     680.000000  8.900000e+02  \n",
       "主账户贷款次数              0.000000       1.000000       3.000000  4.530000e+02  \n",
       "主账户有效贷款次数            0.000000       0.000000       1.000000  1.440000e+02  \n",
       "主账户中尚未还清有效贷款         0.000000       0.000000   35899.000000  9.652492e+07  \n",
       "主账户中已批准的贷款           0.000000       0.000000   64000.000000  1.000000e+09  \n",
       "主账户中已发放贷款            0.000000       0.000000   62000.000000  1.000000e+09  \n",
       "次账户贷款次数              0.000000       0.000000       0.000000  5.200000e+01  \n",
       "次账户有效贷款次数            0.000000       0.000000       0.000000  3.600000e+01  \n",
       "次账户中尚未还清有效贷款         0.000000       0.000000       0.000000  3.603285e+07  \n",
       "次账户中已批准贷款            0.000000       0.000000       0.000000  2.688820e+07  \n",
       "次账户中已发放贷款            0.000000       0.000000       0.000000  2.688820e+07  \n",
       "主账户每月还款              0.000000       0.000000    2000.000000  2.564281e+07  \n",
       "次账户没用还款              0.000000       0.000000       0.000000  3.246710e+06  \n",
       "近六个月新贷款次数            0.000000       0.000000       0.000000  3.500000e+01  \n",
       "近六个月违约次数             0.000000       0.000000       0.000000  2.000000e+01  \n",
       "平均贷款期限               0.000000       0.000000      13.000000  1.170000e+02  \n",
       "第一次贷款距今时间            0.000000       0.000000      20.000000  1.170000e+02  \n",
       "贷款查询次数               0.000000       0.000000       0.000000  2.800000e+01  \n",
       "是否违约                 0.000000       0.000000       0.000000  1.000000e+00  \n",
       "贷款与资产比               0.664431       0.741715       0.809512  9.379874e-01  \n",
       "贷款总次数                0.000000       1.000000       3.000000  4.530000e+02  \n",
       "主账户无效贷款次数            0.000000       0.000000       1.000000  4.510000e+02  \n",
       "次账户无效贷款次数            0.000000       0.000000       0.000000  4.200000e+01  \n",
       "无效贷款总次数              0.000000       0.000000       1.000000  4.510000e+02  \n",
       "尚未还清有效贷款总额           0.000000       0.000000   38189.000000  9.652492e+07  \n",
       "已批准贷款总额              0.000000       0.000000   67206.000000  1.000000e+09  \n",
       "已发放贷款总额              0.000000       0.000000   65085.000000  1.000000e+09  \n",
       "每月还款总额               0.000000       0.000000    2094.000000  2.564281e+07  \n",
       "贷款与已还贷款比列            1.000000       1.000000       1.260000  5.000001e+06  \n",
       "主账户还款期数              0.000000       0.000000      25.000000  1.000000e+09  \n",
       "次账户还款期数              0.000000       0.000000       0.000000  1.980000e+07  \n",
       "贷款与已批准贷款比列           1.000000       1.000000       1.000000  5.000000e+07  \n",
       "总贷款次数与总有效贷款次数比       1.000000       1.000000       1.670000  1.800000e+01  \n",
       "工作类型                 0.000000       0.000000       1.000000  2.000000e+00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.replace([np.inf, -np.inf], np.nan)\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff91200",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cd6c160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "客户编号              0\n",
       "已发货款              0\n",
       "资产成本              0\n",
       "贷款与资产比列           0\n",
       "品牌                0\n",
       "骑车销售商             0\n",
       "车厂                0\n",
       "出生日期              0\n",
       "货款日期              0\n",
       "地区                0\n",
       "对接员工编号            0\n",
       "是否填写手机号           0\n",
       "受否填写身份证           0\n",
       "是否出具驾驶证           0\n",
       "是否填写护照            0\n",
       "信用评分              0\n",
       "主账户贷款次数           0\n",
       "主账户有效贷款次数         0\n",
       "主账户中尚未还清有效贷款      0\n",
       "主账户中已批准的贷款        0\n",
       "主账户中已发放贷款         0\n",
       "次账户贷款次数           0\n",
       "次账户有效贷款次数         0\n",
       "次账户中尚未还清有效贷款      0\n",
       "次账户中已批准贷款         0\n",
       "次账户中已发放贷款         0\n",
       "主账户每月还款           0\n",
       "次账户没用还款           0\n",
       "近六个月新贷款次数         0\n",
       "近六个月违约次数          0\n",
       "平均贷款期限            0\n",
       "第一次贷款距今时间         0\n",
       "贷款查询次数            0\n",
       "是否违约              0\n",
       "贷款与资产比            0\n",
       "贷款总次数             0\n",
       "主账户无效贷款次数         0\n",
       "次账户无效贷款次数         0\n",
       "无效贷款总次数           0\n",
       "尚未还清有效贷款总额        0\n",
       "已批准贷款总额           0\n",
       "已发放贷款总额           0\n",
       "每月还款总额            0\n",
       "贷款与已还贷款比列         0\n",
       "主账户还款期数           0\n",
       "次账户还款期数           0\n",
       "贷款与已批准贷款比列        0\n",
       "总贷款次数与总有效贷款次数比    0\n",
       "工作类型              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缺失值\n",
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4715724a",
   "metadata": {},
   "source": [
    "数据质量较好，没有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "389f114b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户编号\n",
      "524288    1\n",
      "551640    1\n",
      "477884    1\n",
      "475837    1\n",
      "481982    1\n",
      "         ..\n",
      "484550    1\n",
      "486599    1\n",
      "464072    1\n",
      "466121    1\n",
      "526335    1\n",
      "Name: 客户编号, Length: 199697, dtype: int64\n",
      "****************************************************************************************************\n",
      "已发货款\n",
      "48349    1844\n",
      "53303    1805\n",
      "51303    1691\n",
      "50303    1673\n",
      "55259    1622\n",
      "         ... \n",
      "50488       1\n",
      "55381       1\n",
      "51287       1\n",
      "46390       1\n",
      "28686       1\n",
      "Name: 已发货款, Length: 22535, dtype: int64\n",
      "****************************************************************************************************\n",
      "资产成本\n",
      "68000    575\n",
      "67000    503\n",
      "72000    474\n",
      "70000    432\n",
      "66000    407\n",
      "        ... \n",
      "58145      1\n",
      "76568      1\n",
      "94995      1\n",
      "92944      1\n",
      "53222      1\n",
      "Name: 资产成本, Length: 43625, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款与资产比列\n",
      "85.00    3655\n",
      "84.99     861\n",
      "79.99     448\n",
      "80.00     408\n",
      "79.90     351\n",
      "         ... \n",
      "90.22       1\n",
      "28.72       1\n",
      "92.79       1\n",
      "24.93       1\n",
      "26.43       1\n",
      "Name: 贷款与资产比列, Length: 6457, dtype: int64\n",
      "****************************************************************************************************\n",
      "品牌\n",
      "2      11321\n",
      "67      9761\n",
      "3       8038\n",
      "5       7791\n",
      "36      7461\n",
      "       ...  \n",
      "217      156\n",
      "261      146\n",
      "84       131\n",
      "111       78\n",
      "158       53\n",
      "Name: 品牌, Length: 82, dtype: int64\n",
      "****************************************************************************************************\n",
      "骑车销售商\n",
      "18317    1191\n",
      "17980    1083\n",
      "14234    1064\n",
      "15694    1063\n",
      "15663    1061\n",
      "         ... \n",
      "24546       1\n",
      "23741       1\n",
      "22845       1\n",
      "24761       1\n",
      "15045       1\n",
      "Name: 骑车销售商, Length: 2935, dtype: int64\n",
      "****************************************************************************************************\n",
      "车厂\n",
      "86     94155\n",
      "45     48365\n",
      "51     23353\n",
      "48     14096\n",
      "49      8758\n",
      "120     8256\n",
      "67      2034\n",
      "145      664\n",
      "153       10\n",
      "152        5\n",
      "156        1\n",
      "Name: 车厂, dtype: int64\n",
      "****************************************************************************************************\n",
      "出生日期\n",
      "1995    9039\n",
      "1994    9036\n",
      "1992    8853\n",
      "1996    8715\n",
      "1990    8589\n",
      "1993    8548\n",
      "1991    8068\n",
      "1988    7902\n",
      "1989    7616\n",
      "1987    7400\n",
      "1986    7262\n",
      "1985    6799\n",
      "1984    6361\n",
      "1997    6328\n",
      "1983    6150\n",
      "1982    6010\n",
      "1980    5806\n",
      "1981    5288\n",
      "1978    5104\n",
      "1979    4858\n",
      "1975    4840\n",
      "1976    4798\n",
      "1977    4573\n",
      "1974    3995\n",
      "1973    3836\n",
      "1972    3740\n",
      "1970    3482\n",
      "1971    3161\n",
      "1969    2737\n",
      "1968    2628\n",
      "1967    2129\n",
      "1965    2052\n",
      "1966    1961\n",
      "1998    1874\n",
      "1964    1519\n",
      "1999    1385\n",
      "1963    1326\n",
      "1962    1266\n",
      "1960    1061\n",
      "1961    1012\n",
      "1959     710\n",
      "2000     533\n",
      "1958     530\n",
      "1957     350\n",
      "1956     300\n",
      "1955     147\n",
      "1954      19\n",
      "1949       1\n",
      "Name: 出生日期, dtype: int64\n",
      "****************************************************************************************************\n",
      "货款日期\n",
      "2018    199697\n",
      "Name: 货款日期, dtype: int64\n",
      "****************************************************************************************************\n",
      "地区\n",
      "4     38451\n",
      "3     29449\n",
      "6     28797\n",
      "13    15055\n",
      "9     13596\n",
      "8     12145\n",
      "5      8719\n",
      "14     7915\n",
      "1      7746\n",
      "7      5806\n",
      "11     5793\n",
      "18     4664\n",
      "15     4402\n",
      "12     3570\n",
      "2      3505\n",
      "17     3388\n",
      "10     3127\n",
      "16     2322\n",
      "19      889\n",
      "20      164\n",
      "21      131\n",
      "22       63\n",
      "Name: 地区, dtype: int64\n",
      "****************************************************************************************************\n",
      "对接员工编号\n",
      "2546    478\n",
      "620     419\n",
      "255     402\n",
      "2153    346\n",
      "130     340\n",
      "       ... \n",
      "770       1\n",
      "3776      1\n",
      "3746      1\n",
      "3780      1\n",
      "3789      1\n",
      "Name: 对接员工编号, Length: 3257, dtype: int64\n",
      "****************************************************************************************************\n",
      "是否填写手机号\n",
      "1    199697\n",
      "Name: 是否填写手机号, dtype: int64\n",
      "****************************************************************************************************\n",
      "受否填写身份证\n",
      "1    199697\n",
      "Name: 受否填写身份证, dtype: int64\n",
      "****************************************************************************************************\n",
      "是否出具驾驶证\n",
      "0    195035\n",
      "1      4662\n",
      "Name: 是否出具驾驶证, dtype: int64\n",
      "****************************************************************************************************\n",
      "是否填写护照\n",
      "0    199269\n",
      "1       428\n",
      "Name: 是否填写护照, dtype: int64\n",
      "****************************************************************************************************\n",
      "信用评分\n",
      "0      99692\n",
      "738     7419\n",
      "300     7353\n",
      "825     6401\n",
      "15      3192\n",
      "       ...  \n",
      "863        1\n",
      "822        1\n",
      "834        1\n",
      "884        1\n",
      "837        1\n",
      "Name: 信用评分, Length: 572, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户贷款次数\n",
      "0      99692\n",
      "1      30016\n",
      "2      16904\n",
      "3      11213\n",
      "4       8060\n",
      "       ...  \n",
      "147        1\n",
      "85         1\n",
      "453        1\n",
      "83         1\n",
      "124        1\n",
      "Name: 主账户贷款次数, Length: 106, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户有效贷款次数\n",
      "0      116994\n",
      "1       36022\n",
      "2       18506\n",
      "3       10585\n",
      "4        6445\n",
      "5        3964\n",
      "6        2422\n",
      "7        1548\n",
      "8        1058\n",
      "9         661\n",
      "10        453\n",
      "11        288\n",
      "12        200\n",
      "13        147\n",
      "14         98\n",
      "15         79\n",
      "16         50\n",
      "17         47\n",
      "18         32\n",
      "19         26\n",
      "20         13\n",
      "21          9\n",
      "23          8\n",
      "24          8\n",
      "22          6\n",
      "25          6\n",
      "28          5\n",
      "26          4\n",
      "31          2\n",
      "32          2\n",
      "34          2\n",
      "144         1\n",
      "43          1\n",
      "52          1\n",
      "27          1\n",
      "42          1\n",
      "37          1\n",
      "65          1\n",
      "Name: 主账户有效贷款次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户中尚未还清有效贷款\n",
      "0          120994\n",
      "800           109\n",
      "400           103\n",
      "30000          85\n",
      "50000          72\n",
      "            ...  \n",
      "353574          1\n",
      "63786           1\n",
      "61739           1\n",
      "359717          1\n",
      "1052556         1\n",
      "Name: 主账户中尚未还清有效贷款, Length: 62763, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户中已批准的贷款\n",
      "0          117923\n",
      "50000        1264\n",
      "30000        1230\n",
      "100000        816\n",
      "25000         808\n",
      "            ...  \n",
      "5916738         1\n",
      "4108000         1\n",
      "145484          1\n",
      "515790          1\n",
      "1933880         1\n",
      "Name: 主账户中已批准的贷款, Length: 39179, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户中已发放贷款\n",
      "0         118016\n",
      "50000       1177\n",
      "30000       1135\n",
      "100000       792\n",
      "40000        671\n",
      "           ...  \n",
      "418401         1\n",
      "940646         1\n",
      "92800          1\n",
      "383638         1\n",
      "6183           1\n",
      "Name: 主账户中已发放贷款, Length: 42236, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户贷款次数\n",
      "0     194649\n",
      "1       2965\n",
      "2        916\n",
      "3        371\n",
      "4        254\n",
      "5        128\n",
      "6        104\n",
      "7         63\n",
      "8         61\n",
      "9         32\n",
      "10        31\n",
      "11        26\n",
      "13        13\n",
      "12        12\n",
      "14        10\n",
      "15         8\n",
      "16         7\n",
      "19         5\n",
      "18         5\n",
      "17         5\n",
      "22         4\n",
      "23         4\n",
      "31         4\n",
      "20         3\n",
      "38         2\n",
      "34         2\n",
      "30         2\n",
      "21         2\n",
      "29         1\n",
      "28         1\n",
      "25         1\n",
      "24         1\n",
      "35         1\n",
      "37         1\n",
      "42         1\n",
      "46         1\n",
      "52         1\n",
      "Name: 次账户贷款次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户有效贷款次数\n",
      "0     196412\n",
      "1       2313\n",
      "2        550\n",
      "3        175\n",
      "4         90\n",
      "5         54\n",
      "6         26\n",
      "7         19\n",
      "8         15\n",
      "9         10\n",
      "11         7\n",
      "10         7\n",
      "12         5\n",
      "15         3\n",
      "13         2\n",
      "16         2\n",
      "22         2\n",
      "14         1\n",
      "17         1\n",
      "20         1\n",
      "21         1\n",
      "36         1\n",
      "Name: 次账户有效贷款次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户中尚未还清有效贷款\n",
      "0        196800\n",
      "800           9\n",
      "400           7\n",
      "100           7\n",
      "1200          6\n",
      "          ...  \n",
      "4910          1\n",
      "2989          1\n",
      "37950         1\n",
      "48443         1\n",
      "2047          1\n",
      "Name: 次账户中尚未还清有效贷款, Length: 2805, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户中已批准贷款\n",
      "0         196486\n",
      "50000         71\n",
      "100000        51\n",
      "30000         39\n",
      "40000         34\n",
      "           ...  \n",
      "14250          1\n",
      "18340          1\n",
      "337800         1\n",
      "26400          1\n",
      "5436           1\n",
      "Name: 次账户中已批准贷款, Length: 1946, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户中已发放贷款\n",
      "0         196517\n",
      "50000         49\n",
      "100000        40\n",
      "200000        31\n",
      "300000        26\n",
      "           ...  \n",
      "7471           1\n",
      "17700          1\n",
      "33980          1\n",
      "50100          1\n",
      "42859          1\n",
      "Name: 次账户中已发放贷款, Length: 2231, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户每月还款\n",
      "0          136477\n",
      "1620          239\n",
      "1500          134\n",
      "1600          125\n",
      "2000          118\n",
      "            ...  \n",
      "4967483         1\n",
      "16278           1\n",
      "10133           1\n",
      "12180           1\n",
      "35025           1\n",
      "Name: 主账户每月还款, Length: 25704, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户没用还款\n",
      "0        197764\n",
      "1100          6\n",
      "1065          6\n",
      "1167          5\n",
      "2100          5\n",
      "          ...  \n",
      "428           1\n",
      "31139         1\n",
      "2349          1\n",
      "44            1\n",
      "1919          1\n",
      "Name: 次账户没用还款, Length: 1690, dtype: int64\n",
      "****************************************************************************************************\n",
      "近六个月新贷款次数\n",
      "0     155061\n",
      "1      27757\n",
      "2       9474\n",
      "3       3874\n",
      "4       1679\n",
      "5        839\n",
      "6        413\n",
      "7        270\n",
      "8        128\n",
      "9         71\n",
      "10        49\n",
      "11        23\n",
      "12        16\n",
      "13        13\n",
      "14         9\n",
      "16         5\n",
      "17         5\n",
      "20         3\n",
      "18         2\n",
      "23         2\n",
      "15         1\n",
      "21         1\n",
      "28         1\n",
      "35         1\n",
      "Name: 近六个月新贷款次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "近六个月违约次数\n",
      "0     184327\n",
      "1      12653\n",
      "2       2054\n",
      "3        459\n",
      "4        111\n",
      "5         48\n",
      "6         19\n",
      "7         11\n",
      "8          7\n",
      "9          2\n",
      "10         2\n",
      "12         2\n",
      "11         1\n",
      "20         1\n",
      "Name: 近六个月违约次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "平均贷款期限\n",
      "0      101753\n",
      "1       10859\n",
      "13       7265\n",
      "6        5180\n",
      "7        4639\n",
      "        ...  \n",
      "112        10\n",
      "114        10\n",
      "104         9\n",
      "108         6\n",
      "116         6\n",
      "Name: 平均贷款期限, Length: 100, dtype: int64\n",
      "****************************************************************************************************\n",
      "第一次贷款距今时间\n",
      "0      101552\n",
      "13       7091\n",
      "25       6391\n",
      "1        5846\n",
      "6        4112\n",
      "        ...  \n",
      "110        75\n",
      "116        75\n",
      "114        72\n",
      "117        70\n",
      "113        66\n",
      "Name: 第一次贷款距今时间, Length: 100, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款查询次数\n",
      "0     173221\n",
      "1      18971\n",
      "2       4608\n",
      "3       1468\n",
      "4        645\n",
      "5        271\n",
      "6        196\n",
      "7        114\n",
      "8         78\n",
      "9         35\n",
      "10        30\n",
      "12        13\n",
      "11        13\n",
      "14         6\n",
      "15         5\n",
      "13         4\n",
      "17         4\n",
      "18         4\n",
      "19         4\n",
      "16         3\n",
      "20         1\n",
      "22         1\n",
      "23         1\n",
      "28         1\n",
      "Name: 贷款查询次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "是否违约\n",
      "0    164270\n",
      "1     35427\n",
      "Name: 是否违约, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款与资产比\n",
      "0.761323    133\n",
      "0.751046     61\n",
      "0.717988     57\n",
      "0.720886     50\n",
      "0.759402     46\n",
      "           ... \n",
      "0.604606      1\n",
      "0.743955      1\n",
      "0.854449      1\n",
      "0.774318      1\n",
      "0.715263      1\n",
      "Name: 贷款与资产比, Length: 180388, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款总次数\n",
      "0      98703\n",
      "1      29896\n",
      "2      16900\n",
      "3      11331\n",
      "4       8177\n",
      "       ...  \n",
      "85         1\n",
      "453        1\n",
      "83         1\n",
      "82         1\n",
      "124        1\n",
      "Name: 贷款总次数, Length: 106, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户无效贷款次数\n",
      "0      129270\n",
      "1       26433\n",
      "2       12878\n",
      "3        7681\n",
      "4        5196\n",
      "        ...  \n",
      "78          1\n",
      "77          1\n",
      "75          1\n",
      "451         1\n",
      "249         1\n",
      "Name: 主账户无效贷款次数, Length: 96, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户无效贷款次数\n",
      "0     196713\n",
      "1       1847\n",
      "2        518\n",
      "3        200\n",
      "4        127\n",
      "5         88\n",
      "6         63\n",
      "7         42\n",
      "8         22\n",
      "9         17\n",
      "10        13\n",
      "13         9\n",
      "11         6\n",
      "15         6\n",
      "14         5\n",
      "12         4\n",
      "16         4\n",
      "17         2\n",
      "22         2\n",
      "18         1\n",
      "19         1\n",
      "25         1\n",
      "26         1\n",
      "28         1\n",
      "33         1\n",
      "34         1\n",
      "37         1\n",
      "42         1\n",
      "Name: 次账户无效贷款次数, dtype: int64\n",
      "****************************************************************************************************\n",
      "无效贷款总次数\n",
      "0      128284\n",
      "1       26451\n",
      "2       13074\n",
      "3        7872\n",
      "4        5295\n",
      "        ...  \n",
      "68          1\n",
      "60          1\n",
      "129         1\n",
      "146         1\n",
      "249         1\n",
      "Name: 无效贷款总次数, Length: 97, dtype: int64\n",
      "****************************************************************************************************\n",
      "尚未还清有效贷款总额\n",
      "0          119921\n",
      "800           109\n",
      "400           104\n",
      "30000          86\n",
      "50000          71\n",
      "            ...  \n",
      "4655055         1\n",
      "784337          1\n",
      "143338          1\n",
      "141291          1\n",
      "48808           1\n",
      "Name: 尚未还清有效贷款总额, Length: 63778, dtype: int64\n",
      "****************************************************************************************************\n",
      "已批准贷款总额\n",
      "0          116835\n",
      "50000        1266\n",
      "30000        1229\n",
      "100000        820\n",
      "25000         814\n",
      "            ...  \n",
      "581294          1\n",
      "50861           1\n",
      "65194           1\n",
      "2404000         1\n",
      "156300          1\n",
      "Name: 已批准贷款总额, Length: 40012, dtype: int64\n",
      "****************************************************************************************************\n",
      "已发放贷款总额\n",
      "0         116926\n",
      "50000       1183\n",
      "30000       1133\n",
      "100000       792\n",
      "40000        673\n",
      "           ...  \n",
      "347140         1\n",
      "78859          1\n",
      "31778          1\n",
      "29731          1\n",
      "6183           1\n",
      "Name: 已发放贷款总额, Length: 43124, dtype: int64\n",
      "****************************************************************************************************\n",
      "每月还款总额\n",
      "0        135636\n",
      "1620        239\n",
      "1500        134\n",
      "1600        125\n",
      "2000        117\n",
      "          ...  \n",
      "21448         1\n",
      "17354         1\n",
      "19403         1\n",
      "25550         1\n",
      "22731         1\n",
      "Name: 每月还款总额, Length: 26121, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款与已还贷款比列\n",
      "1.00        122309\n",
      "1.02          1075\n",
      "1.01          1061\n",
      "1.05          1039\n",
      "1.04          1029\n",
      "             ...  \n",
      "13031.00         1\n",
      "28891.00         1\n",
      "46951.00         1\n",
      "866.35           1\n",
      "8.36             1\n",
      "Name: 贷款与已还贷款比列, Length: 5210, dtype: int64\n",
      "****************************************************************************************************\n",
      "主账户还款期数\n",
      "0         120664\n",
      "5           3129\n",
      "6           1903\n",
      "3           1690\n",
      "9           1638\n",
      "           ...  \n",
      "220486         1\n",
      "208200         1\n",
      "195954         1\n",
      "97666          1\n",
      "6183           1\n",
      "Name: 主账户还款期数, Length: 16284, dtype: int64\n",
      "****************************************************************************************************\n",
      "次账户还款期数\n",
      "0        196606\n",
      "5            62\n",
      "11           47\n",
      "9            43\n",
      "19           37\n",
      "          ...  \n",
      "61486         1\n",
      "49192         1\n",
      "32800         1\n",
      "38589         1\n",
      "90477         1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: 次账户还款期数, Length: 1573, dtype: int64\n",
      "****************************************************************************************************\n",
      "贷款与已批准贷款比列\n",
      "1.00         189338\n",
      "0.99            864\n",
      "1.01            711\n",
      "0.98            588\n",
      "0.97            462\n",
      "              ...  \n",
      "940001.00         1\n",
      "45001.00          1\n",
      "2.43              1\n",
      "17001.00          1\n",
      "140001.00         1\n",
      "Name: 贷款与已批准贷款比列, Length: 431, dtype: int64\n",
      "****************************************************************************************************\n",
      "总贷款次数与总有效贷款次数比\n",
      "1.00    115897\n",
      "2.00     26689\n",
      "1.50     10429\n",
      "3.00      7571\n",
      "1.33      4843\n",
      "         ...  \n",
      "4.60         1\n",
      "5.25         1\n",
      "2.04         1\n",
      "5.75         1\n",
      "3.30         1\n",
      "Name: 总贷款次数与总有效贷款次数比, Length: 222, dtype: int64\n",
      "****************************************************************************************************\n",
      "工作类型\n",
      "0    108931\n",
      "1     84185\n",
      "2      6581\n",
      "Name: 工作类型, dtype: int64\n",
      "****************************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "#查看每个类别包含多少中数据值\n",
    "for i in df.columns:\n",
    "    print(i)\n",
    "    print(df[i].value_counts())\n",
    "    print('*'*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f7fb29",
   "metadata": {},
   "source": [
    "其中：\n",
    "货款日期,是否填写手机号,受否填写身份证\n",
    "这三类属性都只有1个值，不具有代表性，可以删除\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48dd93df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dd0c4af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5726e9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#查看并筛选相关性系数   删除相关性较高的参数\n",
    "\n",
    "def higt_cor(x,y=0.8):\n",
    "    data_cor = (x.corr()>y)\n",
    "    a=[]\n",
    "    \n",
    "    for i in data_cor.columns:\n",
    "        if data_cor[i].sum()>=2:\n",
    "            a.append(i)\n",
    "\n",
    "    return a  #这些是我们要考虑删除的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5668003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['贷款与资产比列',\n",
       " '主账户贷款次数',\n",
       " '主账户中尚未还清有效贷款',\n",
       " '主账户中已批准的贷款',\n",
       " '主账户中已发放贷款',\n",
       " '次账户贷款次数',\n",
       " '次账户有效贷款次数',\n",
       " '次账户中尚未还清有效贷款',\n",
       " '次账户中已批准贷款',\n",
       " '次账户中已发放贷款',\n",
       " '主账户每月还款',\n",
       " '贷款与资产比',\n",
       " '贷款总次数',\n",
       " '主账户无效贷款次数',\n",
       " '次账户无效贷款次数',\n",
       " '无效贷款总次数',\n",
       " '尚未还清有效贷款总额',\n",
       " '已批准贷款总额',\n",
       " '已发放贷款总额',\n",
       " '每月还款总额',\n",
       " '主账户还款期数']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higt_cor(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4729b546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_data =df[higt_cor(df)]\n",
    "\n",
    "corr = corr_data.corr()\n",
    "plt.figure(figsize=(14,14))\n",
    "sns.heatmap(data=corr,xticklabels=corr.columns,yticklabels=corr.columns,linewidths=0.2,annot=True,cmap='YlGnBu')\n",
    "plt.title('相关系数',size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4261a1",
   "metadata": {},
   "source": [
    "根据相关系数情况，筛选出相关性较高的指标，选择需要保留的指标\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7c8247c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除特征\n",
    "df = df.drop(columns=['货款日期',\n",
    "                        '是否填写手机号',\n",
    "                        '受否填写身份证',\n",
    "                        '是否出具驾驶证',\n",
    "                        '是否填写护照',\n",
    "                        '主账户贷款次数',\n",
    "                        '主账户中已批准的贷款',\n",
    "                        '次账户中已批准贷款',\n",
    "                        '主账户无效贷款次数',\n",
    "                        '已批准贷款总额',\n",
    "                        '次账户还款期数',\n",
    "                        '贷款与已批准贷款比列',\n",
    "                       '次账户中已发放贷款',\n",
    "                       '次账户没用还款',\n",
    "                       '次账户贷款次数',\n",
    "                       '次账户有效贷款次数',\n",
    "                       '次账户无效贷款次数',\n",
    "                       '客户编号'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70ea87c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199697, 31)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "280a3f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199575, 31)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除重复值\n",
    "df.drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e58e00a",
   "metadata": {},
   "source": [
    "## 衍生字段"
   ]
  },
  {
   "cell_type": "raw",
   "id": "33bdaec3",
   "metadata": {},
   "source": [
    "主账户中尚未还清有效贷款   1还清  0未还清\n",
    "主账户中已发放贷款       1发放  0未发放\n",
    "次账户中尚未还清有效贷款   1还清  0未还清\n",
    "主账户每月还款          1还款  0未还款\n",
    "已发放贷款总额          1发放  0未发放\n",
    "每月还款总额           1还款  0未还款"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56a45693",
   "metadata": {},
   "outputs": [],
   "source": [
    "#主账户中尚未还清有效贷款   1还清  0未还清\n",
    "df['主账户中尚未还清有效贷款'] = df['主账户中尚未还清有效贷款'].apply(lambda x:1 if x==0 else 0 )\n",
    "\n",
    "df['主账户中已发放贷款'] = df['主账户中已发放贷款'].apply(lambda x:0 if x==0 else 1 )\n",
    "\n",
    "df['次账户中尚未还清有效贷款'] = df['次账户中尚未还清有效贷款'].apply(lambda x:1 if x==0 else 0 )\n",
    "\n",
    "df['主账户每月还款'] = df['主账户每月还款'].apply(lambda x:0 if x==0 else 1 )\n",
    "\n",
    "df['已发放贷款总额'] = df['已发放贷款总额'].apply(lambda x:0 if x==0 else 1 )\n",
    "\n",
    "df['每月还款总额'] = df['每月还款总额'].apply(lambda x:0 if x==0 else 1 )\n",
    "\n",
    "df['尚未还清有效贷款总额'] = df['尚未还清有效贷款总额'].apply(lambda x:1 if x==0 else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79b6b01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    135636\n",
       "1     64061\n",
       "Name: 每月还款总额, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.每月还款总额.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9042becc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <th>品牌</th>\n",
       "      <th>骑车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>对接员工编号</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>...</th>\n",
       "      <th>贷款与资产比</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65532</td>\n",
       "      <td>78990</td>\n",
       "      <td>84.38</td>\n",
       "      <td>136</td>\n",
       "      <td>20490</td>\n",
       "      <td>45</td>\n",
       "      <td>1981</td>\n",
       "      <td>8</td>\n",
       "      <td>2801</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.829624</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56759</td>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>61</td>\n",
       "      <td>22778</td>\n",
       "      <td>86</td>\n",
       "      <td>1967</td>\n",
       "      <td>6</td>\n",
       "      <td>3060</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.868871</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>59</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58413</td>\n",
       "      <td>67960</td>\n",
       "      <td>89.02</td>\n",
       "      <td>5</td>\n",
       "      <td>15663</td>\n",
       "      <td>86</td>\n",
       "      <td>1977</td>\n",
       "      <td>9</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.859520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72317</td>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>76</td>\n",
       "      <td>17242</td>\n",
       "      <td>48</td>\n",
       "      <td>1995</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.724982</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>13813</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50078</td>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>146</td>\n",
       "      <td>14181</td>\n",
       "      <td>45</td>\n",
       "      <td>1974</td>\n",
       "      <td>17</td>\n",
       "      <td>1828</td>\n",
       "      <td>379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.765134</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.18</td>\n",
       "      <td>42</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199712</th>\n",
       "      <td>36439</td>\n",
       "      <td>60424</td>\n",
       "      <td>62.89</td>\n",
       "      <td>10</td>\n",
       "      <td>23507</td>\n",
       "      <td>45</td>\n",
       "      <td>1986</td>\n",
       "      <td>3</td>\n",
       "      <td>121</td>\n",
       "      <td>753</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603055</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>525000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199713</th>\n",
       "      <td>52303</td>\n",
       "      <td>72677</td>\n",
       "      <td>72.93</td>\n",
       "      <td>34</td>\n",
       "      <td>15142</td>\n",
       "      <td>86</td>\n",
       "      <td>1985</td>\n",
       "      <td>6</td>\n",
       "      <td>1641</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.719664</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199714</th>\n",
       "      <td>54413</td>\n",
       "      <td>62710</td>\n",
       "      <td>89.30</td>\n",
       "      <td>67</td>\n",
       "      <td>16565</td>\n",
       "      <td>45</td>\n",
       "      <td>1973</td>\n",
       "      <td>6</td>\n",
       "      <td>1071</td>\n",
       "      <td>771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867693</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.03</td>\n",
       "      <td>487</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199715</th>\n",
       "      <td>54509</td>\n",
       "      <td>71921</td>\n",
       "      <td>77.86</td>\n",
       "      <td>74</td>\n",
       "      <td>16846</td>\n",
       "      <td>45</td>\n",
       "      <td>1983</td>\n",
       "      <td>4</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.757901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199716</th>\n",
       "      <td>63147</td>\n",
       "      <td>72000</td>\n",
       "      <td>89.58</td>\n",
       "      <td>2</td>\n",
       "      <td>23169</td>\n",
       "      <td>45</td>\n",
       "      <td>1970</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.877042</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>106508</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199697 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         已发货款   资产成本  贷款与资产比列   品牌  骑车销售商  车厂  出生日期  地区  对接员工编号  信用评分  ...  \\\n",
       "0       65532  78990    84.38  136  20490  45  1981   8    2801     0  ...   \n",
       "1       56759  65325    89.55   61  22778  86  1967   6    3060   300  ...   \n",
       "2       58413  67960    89.02    5  15663  86  1977   9    1032     0  ...   \n",
       "3       72317  99750    73.68   76  17242  48  1995   8     220   763  ...   \n",
       "4       50078  65450    79.45  146  14181  45  1974  17    1828   379  ...   \n",
       "...       ...    ...      ...  ...    ...  ..   ...  ..     ...   ...  ...   \n",
       "199712  36439  60424    62.89   10  23507  45  1986   3     121   753  ...   \n",
       "199713  52303  72677    72.93   34  15142  86  1985   6    1641     0  ...   \n",
       "199714  54413  62710    89.30   67  16565  45  1973   6    1071   771  ...   \n",
       "199715  54509  71921    77.86   74  16846  45  1983   4     306     0  ...   \n",
       "199716  63147  72000    89.58    2  23169  45  1970   4      57   708  ...   \n",
       "\n",
       "          贷款与资产比  贷款总次数  无效贷款总次数  尚未还清有效贷款总额  已发放贷款总额  每月还款总额  贷款与已还贷款比列  \\\n",
       "0       0.829624      0        0           1        0       0       1.00   \n",
       "1       0.868871      7        5           0        1       1       0.99   \n",
       "2       0.859520      0        0           1        0       0       1.00   \n",
       "3       0.724982      1        0           1        1       0   13814.00   \n",
       "4       0.765134     16       15           0        1       1       1.18   \n",
       "...          ...    ...      ...         ...      ...     ...        ...   \n",
       "199712  0.603055      2        0           0        1       0       0.89   \n",
       "199713  0.719664      0        0           1        0       0       1.00   \n",
       "199714  0.867693      2        0           0        1       1       1.03   \n",
       "199715  0.757901      0        0           1        0       0       1.00   \n",
       "199716  0.877042      3        1           0        1       0       1.09   \n",
       "\n",
       "        主账户还款期数  总贷款次数与总有效贷款次数比  工作类型  \n",
       "0             0            1.00     0  \n",
       "1            59            1.33     1  \n",
       "2             0            1.00     1  \n",
       "3         13813            2.00     0  \n",
       "4            42            1.06     1  \n",
       "...         ...             ...   ...  \n",
       "199712   525000            3.00     0  \n",
       "199713        0            1.00     0  \n",
       "199714      487            3.00     1  \n",
       "199715        0            1.00     1  \n",
       "199716   106508            2.00     2  \n",
       "\n",
       "[199697 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ab5ed43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>已发货款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>54256.836542</td>\n",
       "      <td>1.297761e+04</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>46980.000000</td>\n",
       "      <td>53703.00000</td>\n",
       "      <td>60247.000000</td>\n",
       "      <td>9.905720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产成本</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>75824.588552</td>\n",
       "      <td>1.892962e+04</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>65714.000000</td>\n",
       "      <td>70922.00000</td>\n",
       "      <td>79159.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>74.644187</td>\n",
       "      <td>1.149025e+01</td>\n",
       "      <td>10.030000</td>\n",
       "      <td>68.730000</td>\n",
       "      <td>76.67000</td>\n",
       "      <td>83.590000</td>\n",
       "      <td>9.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品牌</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>72.697467</td>\n",
       "      <td>6.970657e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>61.00000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>2.610000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>骑车销售商</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>19634.132491</td>\n",
       "      <td>3.493621e+03</td>\n",
       "      <td>10524.000000</td>\n",
       "      <td>16505.000000</td>\n",
       "      <td>20333.00000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>2.480300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车厂</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>69.085825</td>\n",
       "      <td>2.212828e+01</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.560000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>出生日期</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1983.877349</td>\n",
       "      <td>9.805611e+00</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1986.00000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>7.245206</td>\n",
       "      <td>4.481326e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>对接员工编号</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1547.814529</td>\n",
       "      <td>9.748976e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>1449.00000</td>\n",
       "      <td>2357.000000</td>\n",
       "      <td>3.795000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信用评分</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>291.730912</td>\n",
       "      <td>3.393123e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>8.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.048388</td>\n",
       "      <td>1.951090e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.440000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.605888</td>\n",
       "      <td>4.886604e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已发放贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.409025</td>\n",
       "      <td>4.916551e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.985493</td>\n",
       "      <td>1.195684e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户每月还款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.316580</td>\n",
       "      <td>4.651430e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.385108</td>\n",
       "      <td>9.573789e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.095955</td>\n",
       "      <td>3.809424e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均贷款期限</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>8.055053</td>\n",
       "      <td>1.385536e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>13.187920</td>\n",
       "      <td>2.115378e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款查询次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.203338</td>\n",
       "      <td>6.940883e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否违约</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.177404</td>\n",
       "      <td>3.820110e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.723577</td>\n",
       "      <td>1.136101e-01</td>\n",
       "      <td>0.094638</td>\n",
       "      <td>0.664432</td>\n",
       "      <td>0.74172</td>\n",
       "      <td>0.809513</td>\n",
       "      <td>9.379874e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款总次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>2.523518</td>\n",
       "      <td>5.356281e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.447443</td>\n",
       "      <td>4.075711e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.600515</td>\n",
       "      <td>4.897938e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.414483</td>\n",
       "      <td>4.926338e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>每月还款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.320791</td>\n",
       "      <td>4.667818e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1373.357578</td>\n",
       "      <td>2.981642e+04</td>\n",
       "      <td>-110000.330000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>5.000001e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户还款期数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>50596.599658</td>\n",
       "      <td>2.275784e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.438891</td>\n",
       "      <td>7.922433e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工作类型</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.487474</td>\n",
       "      <td>5.619204e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std            min  \\\n",
       "已发货款            199697.0  54256.836542  1.297761e+04   13320.000000   \n",
       "资产成本            199697.0  75824.588552  1.892962e+04   37000.000000   \n",
       "贷款与资产比列         199697.0     74.644187  1.149025e+01      10.030000   \n",
       "品牌              199697.0     72.697467  6.970657e+01       1.000000   \n",
       "骑车销售商           199697.0  19634.132491  3.493621e+03   10524.000000   \n",
       "车厂              199697.0     69.085825  2.212828e+01      45.000000   \n",
       "出生日期            199697.0   1983.877349  9.805611e+00    1949.000000   \n",
       "地区              199697.0      7.245206  4.481326e+00       1.000000   \n",
       "对接员工编号          199697.0   1547.814529  9.748976e+02       1.000000   \n",
       "信用评分            199697.0    291.730912  3.393123e+02       0.000000   \n",
       "主账户有效贷款次数       199697.0      1.048388  1.951090e+00       0.000000   \n",
       "主账户中尚未还清有效贷款    199697.0      0.605888  4.886604e-01       0.000000   \n",
       "主账户中已发放贷款       199697.0      0.409025  4.916551e-01       0.000000   \n",
       "次账户中尚未还清有效贷款    199697.0      0.985493  1.195684e-01       0.000000   \n",
       "主账户每月还款         199697.0      0.316580  4.651430e-01       0.000000   \n",
       "近六个月新贷款次数       199697.0      0.385108  9.573789e-01       0.000000   \n",
       "近六个月违约次数        199697.0      0.095955  3.809424e-01       0.000000   \n",
       "平均贷款期限          199697.0      8.055053  1.385536e+01       0.000000   \n",
       "第一次贷款距今时间       199697.0     13.187920  2.115378e+01       0.000000   \n",
       "贷款查询次数          199697.0      0.203338  6.940883e-01       0.000000   \n",
       "是否违约            199697.0      0.177404  3.820110e-01       0.000000   \n",
       "贷款与资产比          199697.0      0.723577  1.136101e-01       0.094638   \n",
       "贷款总次数           199697.0      2.523518  5.356281e+00       0.000000   \n",
       "无效贷款总次数         199697.0      1.447443  4.075711e+00       0.000000   \n",
       "尚未还清有效贷款总额      199697.0      0.600515  4.897938e-01       0.000000   \n",
       "已发放贷款总额         199697.0      0.414483  4.926338e-01       0.000000   \n",
       "每月还款总额          199697.0      0.320791  4.667818e-01       0.000000   \n",
       "贷款与已还贷款比列       199697.0   1373.357578  2.981642e+04 -110000.330000   \n",
       "主账户还款期数         199697.0  50596.599658  2.275784e+06       0.000000   \n",
       "总贷款次数与总有效贷款次数比  199697.0      1.438891  7.922433e-01       1.000000   \n",
       "工作类型            199697.0      0.487474  5.619204e-01       0.000000   \n",
       "\n",
       "                         25%          50%           75%           max  \n",
       "已发货款            46980.000000  53703.00000  60247.000000  9.905720e+05  \n",
       "资产成本            65714.000000  70922.00000  79159.000000  1.628992e+06  \n",
       "贷款与资产比列            68.730000     76.67000     83.590000  9.500000e+01  \n",
       "品牌                 14.000000     61.00000    130.000000  2.610000e+02  \n",
       "骑车销售商           16505.000000  20333.00000  23000.000000  2.480300e+04  \n",
       "车厂                 48.000000     86.00000     86.000000  1.560000e+02  \n",
       "出生日期             1977.000000   1986.00000   1992.000000  2.000000e+03  \n",
       "地区                  4.000000      6.00000     10.000000  2.200000e+01  \n",
       "对接员工编号            712.000000   1449.00000   2357.000000  3.795000e+03  \n",
       "信用评分                0.000000     14.00000    680.000000  8.900000e+02  \n",
       "主账户有效贷款次数           0.000000      0.00000      1.000000  1.440000e+02  \n",
       "主账户中尚未还清有效贷款        0.000000      1.00000      1.000000  1.000000e+00  \n",
       "主账户中已发放贷款           0.000000      0.00000      1.000000  1.000000e+00  \n",
       "次账户中尚未还清有效贷款        1.000000      1.00000      1.000000  1.000000e+00  \n",
       "主账户每月还款             0.000000      0.00000      1.000000  1.000000e+00  \n",
       "近六个月新贷款次数           0.000000      0.00000      0.000000  3.500000e+01  \n",
       "近六个月违约次数            0.000000      0.00000      0.000000  2.000000e+01  \n",
       "平均贷款期限              0.000000      0.00000     13.000000  1.170000e+02  \n",
       "第一次贷款距今时间           0.000000      0.00000     20.000000  1.170000e+02  \n",
       "贷款查询次数              0.000000      0.00000      0.000000  2.800000e+01  \n",
       "是否违约                0.000000      0.00000      0.000000  1.000000e+00  \n",
       "贷款与资产比              0.664432      0.74172      0.809513  9.379874e-01  \n",
       "贷款总次数               0.000000      1.00000      3.000000  4.530000e+02  \n",
       "无效贷款总次数             0.000000      0.00000      1.000000  4.510000e+02  \n",
       "尚未还清有效贷款总额          0.000000      1.00000      1.000000  1.000000e+00  \n",
       "已发放贷款总额             0.000000      0.00000      1.000000  1.000000e+00  \n",
       "每月还款总额              0.000000      0.00000      1.000000  1.000000e+00  \n",
       "贷款与已还贷款比列           1.000000      1.00000      1.260000  5.000001e+06  \n",
       "主账户还款期数             0.000000      0.00000     25.000000  1.000000e+09  \n",
       "总贷款次数与总有效贷款次数比      1.000000      1.00000      1.670000  1.800000e+01  \n",
       "工作类型                0.000000      0.00000      1.000000  2.000000e+00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55e67b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['骑车销售商','车厂','品牌','对接员工编号','出生日期'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "994a86cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00        122309\n",
       "1.02          1075\n",
       "1.01          1061\n",
       "1.05          1039\n",
       "1.04          1029\n",
       "             ...  \n",
       "13031.00         1\n",
       "28891.00         1\n",
       "46951.00         1\n",
       "866.35           1\n",
       "8.36             1\n",
       "Name: 贷款与已还贷款比列, Length: 5210, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.贷款与已还贷款比列.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d893235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "343        -75.44\n",
       "2252        -1.30\n",
       "2846        -3.16\n",
       "3472     -1375.00\n",
       "4414       -64.51\n",
       "           ...   \n",
       "194409      -7.69\n",
       "194924     -13.14\n",
       "198041      -1.50\n",
       "198893   -2416.91\n",
       "199116     -16.52\n",
       "Name: 贷款与已还贷款比列, Length: 337, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.贷款与已还贷款比列<0].贷款与已还贷款比列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a7a10f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.贷款与已还贷款比列 = df.贷款与已还贷款比列.apply(lambda x: x if x>=0 else 0)\n",
    "df.贷款与已还贷款比列 = df.贷款与已还贷款比列.apply(lambda x: x if x<=1 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "93ef60b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>已发货款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>54256.836542</td>\n",
       "      <td>1.297761e+04</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>46980.000000</td>\n",
       "      <td>53703.00000</td>\n",
       "      <td>60247.000000</td>\n",
       "      <td>9.905720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产成本</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>75824.588552</td>\n",
       "      <td>1.892962e+04</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>65714.000000</td>\n",
       "      <td>70922.00000</td>\n",
       "      <td>79159.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>74.644187</td>\n",
       "      <td>1.149025e+01</td>\n",
       "      <td>10.030000</td>\n",
       "      <td>68.730000</td>\n",
       "      <td>76.67000</td>\n",
       "      <td>83.590000</td>\n",
       "      <td>9.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>7.245206</td>\n",
       "      <td>4.481326e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信用评分</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>291.730912</td>\n",
       "      <td>3.393123e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>8.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.048388</td>\n",
       "      <td>1.951090e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.440000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.605888</td>\n",
       "      <td>4.886604e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已发放贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.409025</td>\n",
       "      <td>4.916551e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.985493</td>\n",
       "      <td>1.195684e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户每月还款</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.316580</td>\n",
       "      <td>4.651430e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.385108</td>\n",
       "      <td>9.573789e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.095955</td>\n",
       "      <td>3.809424e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均贷款期限</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>8.055053</td>\n",
       "      <td>1.385536e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>13.187920</td>\n",
       "      <td>2.115378e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款查询次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.203338</td>\n",
       "      <td>6.940883e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否违约</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.177404</td>\n",
       "      <td>3.820110e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.723577</td>\n",
       "      <td>1.136101e-01</td>\n",
       "      <td>0.094638</td>\n",
       "      <td>0.664432</td>\n",
       "      <td>0.74172</td>\n",
       "      <td>0.809513</td>\n",
       "      <td>9.379874e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款总次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>2.523518</td>\n",
       "      <td>5.356281e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.447443</td>\n",
       "      <td>4.075711e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.600515</td>\n",
       "      <td>4.897938e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.414483</td>\n",
       "      <td>4.926338e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>每月还款总额</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.320791</td>\n",
       "      <td>4.667818e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.992206</td>\n",
       "      <td>7.304099e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户还款期数</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>50596.599658</td>\n",
       "      <td>2.275784e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>1.438891</td>\n",
       "      <td>7.922433e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工作类型</th>\n",
       "      <td>199697.0</td>\n",
       "      <td>0.487474</td>\n",
       "      <td>5.619204e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std           min  \\\n",
       "已发货款            199697.0  54256.836542  1.297761e+04  13320.000000   \n",
       "资产成本            199697.0  75824.588552  1.892962e+04  37000.000000   \n",
       "贷款与资产比列         199697.0     74.644187  1.149025e+01     10.030000   \n",
       "地区              199697.0      7.245206  4.481326e+00      1.000000   \n",
       "信用评分            199697.0    291.730912  3.393123e+02      0.000000   \n",
       "主账户有效贷款次数       199697.0      1.048388  1.951090e+00      0.000000   \n",
       "主账户中尚未还清有效贷款    199697.0      0.605888  4.886604e-01      0.000000   \n",
       "主账户中已发放贷款       199697.0      0.409025  4.916551e-01      0.000000   \n",
       "次账户中尚未还清有效贷款    199697.0      0.985493  1.195684e-01      0.000000   \n",
       "主账户每月还款         199697.0      0.316580  4.651430e-01      0.000000   \n",
       "近六个月新贷款次数       199697.0      0.385108  9.573789e-01      0.000000   \n",
       "近六个月违约次数        199697.0      0.095955  3.809424e-01      0.000000   \n",
       "平均贷款期限          199697.0      8.055053  1.385536e+01      0.000000   \n",
       "第一次贷款距今时间       199697.0     13.187920  2.115378e+01      0.000000   \n",
       "贷款查询次数          199697.0      0.203338  6.940883e-01      0.000000   \n",
       "是否违约            199697.0      0.177404  3.820110e-01      0.000000   \n",
       "贷款与资产比          199697.0      0.723577  1.136101e-01      0.094638   \n",
       "贷款总次数           199697.0      2.523518  5.356281e+00      0.000000   \n",
       "无效贷款总次数         199697.0      1.447443  4.075711e+00      0.000000   \n",
       "尚未还清有效贷款总额      199697.0      0.600515  4.897938e-01      0.000000   \n",
       "已发放贷款总额         199697.0      0.414483  4.926338e-01      0.000000   \n",
       "每月还款总额          199697.0      0.320791  4.667818e-01      0.000000   \n",
       "贷款与已还贷款比列       199697.0      0.992206  7.304099e-02      0.000000   \n",
       "主账户还款期数         199697.0  50596.599658  2.275784e+06      0.000000   \n",
       "总贷款次数与总有效贷款次数比  199697.0      1.438891  7.922433e-01      1.000000   \n",
       "工作类型            199697.0      0.487474  5.619204e-01      0.000000   \n",
       "\n",
       "                         25%          50%           75%           max  \n",
       "已发货款            46980.000000  53703.00000  60247.000000  9.905720e+05  \n",
       "资产成本            65714.000000  70922.00000  79159.000000  1.628992e+06  \n",
       "贷款与资产比列            68.730000     76.67000     83.590000  9.500000e+01  \n",
       "地区                  4.000000      6.00000     10.000000  2.200000e+01  \n",
       "信用评分                0.000000     14.00000    680.000000  8.900000e+02  \n",
       "主账户有效贷款次数           0.000000      0.00000      1.000000  1.440000e+02  \n",
       "主账户中尚未还清有效贷款        0.000000      1.00000      1.000000  1.000000e+00  \n",
       "主账户中已发放贷款           0.000000      0.00000      1.000000  1.000000e+00  \n",
       "次账户中尚未还清有效贷款        1.000000      1.00000      1.000000  1.000000e+00  \n",
       "主账户每月还款             0.000000      0.00000      1.000000  1.000000e+00  \n",
       "近六个月新贷款次数           0.000000      0.00000      0.000000  3.500000e+01  \n",
       "近六个月违约次数            0.000000      0.00000      0.000000  2.000000e+01  \n",
       "平均贷款期限              0.000000      0.00000     13.000000  1.170000e+02  \n",
       "第一次贷款距今时间           0.000000      0.00000     20.000000  1.170000e+02  \n",
       "贷款查询次数              0.000000      0.00000      0.000000  2.800000e+01  \n",
       "是否违约                0.000000      0.00000      0.000000  1.000000e+00  \n",
       "贷款与资产比              0.664432      0.74172      0.809513  9.379874e-01  \n",
       "贷款总次数               0.000000      1.00000      3.000000  4.530000e+02  \n",
       "无效贷款总次数             0.000000      0.00000      1.000000  4.510000e+02  \n",
       "尚未还清有效贷款总额          0.000000      1.00000      1.000000  1.000000e+00  \n",
       "已发放贷款总额             0.000000      0.00000      1.000000  1.000000e+00  \n",
       "每月还款总额              0.000000      0.00000      1.000000  1.000000e+00  \n",
       "贷款与已还贷款比列           1.000000      1.00000      1.000000  1.000000e+00  \n",
       "主账户还款期数             0.000000      0.00000     25.000000  1.000000e+09  \n",
       "总贷款次数与总有效贷款次数比      1.000000      1.00000      1.670000  1.800000e+01  \n",
       "工作类型                0.000000      0.00000      1.000000  2.000000e+00  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44fc8667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '工作类型')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#主账户中尚未还清有效贷款 \n",
    "plt.figure(figsize=(14,14))\n",
    "plt.subplot(3,3,1)\n",
    "gender = sns.countplot(x='主账户中尚未还清有效贷款',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('主账户中尚未还清有效贷款',size=10)\n",
    "plt.title('主账户中尚未还清有效贷款',size=10)\n",
    "\n",
    "\n",
    "#主账户中已发放贷款 \n",
    "plt.subplot(3,3,2)\n",
    "gender = sns.countplot(x='主账户中已发放贷款',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('主账户中已发放贷款',size=10)\n",
    "plt.title('主账户中已发放贷款',size=10)\n",
    "\n",
    "\n",
    "#次账户中尚未还清有效贷款 \n",
    "plt.subplot(3,3,3)\n",
    "gender = sns.countplot(x='次账户中尚未还清有效贷款',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('次账户中尚未还清有效贷款',size=10)\n",
    "plt.title('次账户中尚未还清有效贷款',size=10)\n",
    "\n",
    "\n",
    "#主账户每月还款 \n",
    "plt.subplot(3,3,4)\n",
    "gender = sns.countplot(x='主账户每月还款',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('主账户每月还款',size=10)\n",
    "plt.title('主账户每月还款',size=10)\n",
    "\n",
    "#尚未还清有效贷款总额 \n",
    "plt.subplot(3,3,5)\n",
    "gender = sns.countplot(x='尚未还清有效贷款总额',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('尚未还清有效贷款总额',size=10)\n",
    "plt.title('尚未还清有效贷款总额',size=10)\n",
    "\n",
    "#已发放贷款总额 \n",
    "plt.subplot(3,3,6)\n",
    "gender = sns.countplot(x='已发放贷款总额',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('已发放贷款总额',size=10)\n",
    "plt.title('已发放贷款总额',size=10)\n",
    "\n",
    "\n",
    "#每月还款总额 \n",
    "plt.subplot(3,3,7)\n",
    "gender = sns.countplot(x='每月还款总额',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('每月还款总额',size=10)\n",
    "plt.title('每月还款总额',size=10)\n",
    "\n",
    "#工作类型 \n",
    "plt.subplot(3,3,8)\n",
    "gender = sns.countplot(x='工作类型',hue='是否违约',data=df,palette='pastel')\n",
    "plt.xlabel('工作类型',size=10)\n",
    "plt.title('工作类型',size=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ffac3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#由于有图形相似度较高 ，可以尝试取出相似度高的参数，优化模型\n",
    "df = df.drop(columns=['主账户中已发放贷款','主账户每月还款','主账户中尚未还清有效贷款'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dae79e4",
   "metadata": {},
   "source": [
    "## 失衡数据判断并处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "86773caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    164270\n",
       "1     35427\n",
       "Name: 是否违约, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.是否违约.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29331861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2156443827645186"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "35428/164289"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cde543bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据集中有 164289  违约用户， 35428 没有违约用户  比例高于10% 没有严重不均衡\n",
    "#所以可以不用处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4322a568",
   "metadata": {},
   "source": [
    "## 多个备选模型比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9588e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "# 模型处理模块\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# 标准化处理模块\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 常规模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# 集成学习和stacking模型\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "# 评价标准模块\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,recall_score,precision_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0303159e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "42770898",
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分测试集与训练集\n",
    "X = df.drop(columns='是否违约')\n",
    "Y = df.是否违约\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b66cf91e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#归一化处理数据\n",
    "\n",
    "X_train = StandardScaler().fit_transform(X_train)\n",
    "X_test = StandardScaler().fit_transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "177f6621",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(X_train, Y_train, X_test, Y_test,\n",
    "               model,model_name):\n",
    "    \n",
    "    print('训练{}'.format(model_name))\n",
    "    \n",
    "    clf=model\n",
    "    start = time.time()\n",
    "    clf.fit(X_train, Y_train.values.ravel())\n",
    "    \n",
    "     #验证模型\n",
    "    print('训练准确率：{:.4f}'.format(clf.score(X_train, Y_train)))\n",
    "    \n",
    "    \n",
    "    predict=clf.predict(X_test)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    precision=precision_score(Y_test,predict)\n",
    "    recall=recall_score(Y_test,predict)\n",
    "    print('测试准确率：{:.4f}'.format(score))\n",
    "    print('测试精确率：{:.4f}'.format(precision))\n",
    "    print('测试召回率：{:.4f}'.format(recall))\n",
    "    \n",
    "    end = time.time()\n",
    "    duration = end - start\n",
    "    print('模型训练耗时：{:6f}s'.format(duration))\n",
    "    \n",
    "    \n",
    "    return clf, score,precision,recall, duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5e6e0672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练LR\n",
      "训练准确率：0.8221\n",
      "测试准确率：0.8231\n",
      "测试精确率：0.3333\n",
      "测试召回率：0.0014\n",
      "模型训练耗时：0.954069s\n",
      "训练DT\n",
      "训练准确率：0.8263\n",
      "测试准确率：0.8175\n",
      "测试精确率：0.2160\n",
      "测试召回率：0.0126\n",
      "模型训练耗时：0.942036s\n",
      "训练AdaBoost\n",
      "训练准确率：0.8224\n",
      "测试准确率：0.8231\n",
      "测试精确率：0.2500\n",
      "测试召回率：0.0009\n",
      "模型训练耗时：8.038716s\n",
      "训练GBDT\n",
      "训练准确率：0.8225\n",
      "测试准确率：0.8232\n",
      "测试精确率：0.3704\n",
      "测试召回率：0.0014\n",
      "模型训练耗时：27.292475s\n",
      "训练RF\n",
      "训练准确率：0.9951\n",
      "测试准确率：0.8170\n",
      "测试精确率：0.2821\n",
      "测试召回率：0.0234\n",
      "模型训练耗时：33.046885s\n",
      "训练XGBoost\n",
      "[23:28:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "训练准确率：0.8265\n",
      "测试准确率：0.8223\n",
      "测试精确率：0.2970\n",
      "测试召回率：0.0043\n",
      "模型训练耗时：6.226172s\n"
     ]
    }
   ],
   "source": [
    "model_name_param_dict = {    'LR': (LogisticRegression(penalty =\"l2\")),\n",
    "                             'DT': (DecisionTreeClassifier(max_depth=10,min_samples_split=10)),\n",
    "                             'AdaBoost': (AdaBoostClassifier()),\n",
    "                             'GBDT': (GradientBoostingClassifier()),\n",
    "                             'RF': (RandomForestClassifier()),\n",
    "                             'XGBoost':(XGBClassifier())\n",
    "                         }\n",
    "\n",
    "result_df = pd.DataFrame(columns=['Accuracy (%)','precision(%)','recall(%)','Time (s)'],\n",
    "                             index=list(model_name_param_dict.keys()))\n",
    "\n",
    "for model_name, model in model_name_param_dict.items():\n",
    "    clf, acc,pre,recall, mean_duration = train_model(X_train, Y_train,\n",
    "                                                        X_test, Y_test,\n",
    "                                                        model,model_name)\n",
    "    result_df.loc[model_name, 'Accuracy (%)'] = acc\n",
    "    result_df.loc[model_name, 'precision(%)'] = pre\n",
    "    result_df.loc[model_name, 'recall(%)'] = recall\n",
    "    result_df.loc[model_name, 'Time (s)'] = mean_duration \n",
    "\n",
    "result_df.to_csv(os.path.join('model_comparison.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e3f0ee7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 12,\n",
       " 'max_features': 10,\n",
       " 'min_samples_leaf': 30,\n",
       " 'min_samples_split': 30,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param_grid = {'n_estimators': [10, 50,100], 'max_features': [10,30,60],\"max_depth\":[4,8,12],\n",
    "             \"min_samples_split\": [10,30,40],\"min_samples_leaf\": [5,20,30]},\n",
    "\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='roc_auc')\n",
    "result = grid_search.fit(X_train, Y_train)\n",
    "result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1fa5a633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6404737158041661"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a3ec6fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 12,\n",
       " 'max_features': 10,\n",
       " 'min_samples_leaf': 30,\n",
       " 'min_samples_split': 30,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param_grid = {'n_estimators': [100], 'max_features': [10],\"max_depth\":[12],\n",
    "             \"min_samples_split\": [30],\"min_samples_leaf\": [30]},\n",
    "\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='roc_auc')\n",
    "result = grid_search.fit(X_train, Y_train)\n",
    "result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "626f2824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6401570080820568"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1ae569a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "#精准率和召回率    在提供的有限参数里，模型质量没有默认的好\n",
    "print(precision_score(Y_test,pre),recall_score(Y_test,pre))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571916b6",
   "metadata": {},
   "source": [
    "模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0f4486",
   "metadata": {},
   "outputs": [],
   "source": [
    "#优质模型保存\n",
    "from sklearn.externals import joblib\n",
    "#保存模型\n",
    "joblib.dump(temp,'model.model')\n",
    "\n",
    "#加载模型\n",
    "#clf=joblib.load('model.model')"
   ]
  }
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